Datasets:
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---
dataset_info:
features:
- name: idx
dtype: string
- name: question
dtype: string
- name: options
struct:
- name: A
dtype: string
- name: B
dtype: string
- name: C
dtype: string
- name: D
dtype: string
- name: E
dtype: string
- name: answer
dtype: string
- name: explanation
dtype: string
- name: link
dtype: string
splits:
- name: op4_test
num_bytes: 1210993
num_examples: 308
- name: op5_test
num_bytes: 1218063
num_examples: 308
download_size: 1206626
dataset_size: 2429056
configs:
- config_name: default
data_files:
- split: op4_test
path: data/op4_test-*
- split: op5_test
path: data/op5_test-*
task_categories:
- question-answering
tags:
- medical,
- clinical,
- multiple-choice
- usmle
---
# Medbullets
HuggingFace upload of a multiple-choice QA dataset of USMLE Step 2 and Step 3 style questions sourced from [Medbullets](https://step2.medbullets.com/). If used, please cite the original authors using the citation below.
## Dataset Details
### Dataset Description
The dataset contains four splits:
- **op4_test**: four-option multiple-choice QA (choices A-D)
- **op5_test**: five-option multiple-choice QA (choices A-E)
`op5_test` contains the same content as `op4_test`, but with one additional answer choice to increase difficulty. Note that while the content is the same, the letter choice corresponding to the correct answer is sometimes different between these splits.
### Dataset Sources
- **Repository:** https://github.com/HanjieChen/ChallengeClinicalQA
- **Paper:** https://arxiv.org/pdf/2402.18060v3
### Direct Use
```python
import json
from datasets import load_dataset, Dataset
def _strip_E(split):
for ex in split:
ex = dict(ex)
ex["options"] = {k: v for k, v in ex["options"].items() if k != "E"}
yield ex
if __name__ == "__main__":
op4_test, op5_test = load_dataset("mkieffer/Medbullets", split=["op4_test", "op5_test"])
# remove the "E" option from op4 split
op4_test = Dataset.from_generator(lambda: _strip_E(op4_test))
print("\nop4_test:\n", json.dumps(op4_test[0], indent=2))
print("\nop5_test:\n", json.dumps(op5_test[0], indent=2))
```
## Citation
```
@inproceedings{chen-etal-2025-benchmarking,
title = "Benchmarking Large Language Models on Answering and Explaining Challenging Medical Questions",
author = "Chen, Hanjie and
Fang, Zhouxiang and
Singla, Yash and
Dredze, Mark",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.182/",
doi = "10.18653/v1/2025.naacl-long.182",
pages = "3563--3599",
ISBN = "979-8-89176-189-6",
abstract = "LLMs have demonstrated impressive performance in answering medical questions, such as achieving passing scores on medical licensing examinations. However, medical board exams or general clinical questions do not capture the complexity of realistic clinical cases. Moreover, the lack of reference explanations means we cannot easily evaluate the reasoning of model decisions, a crucial component of supporting doctors in making complex medical decisions. To address these challenges, we construct two new datasets: JAMA Clinical Challenge and Medbullets. JAMA Clinical Challenge consists of questions based on challenging clinical cases, while Medbullets comprises simulated clinical questions. Both datasets are structured as multiple-choice question-answering tasks, accompanied by expert-written explanations. We evaluate seven LLMs on the two datasets using various prompts. Experiments demonstrate that our datasets are harder than previous benchmarks. In-depth automatic and human evaluations of model-generated explanations provide insights into the promise and deficiency of LLMs for explainable medical QA."
}
``` |